IATanalytics - Compute Effect Sizes and Reliability for Implicit Association
Test (IAT) Data
Quickly score raw data outputted from an Implicit
Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998)
<doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as
specified by Greenwald, Nosek, and Banaji (2003)
<doi:10.1037/0022-3514.85.2.197>. The output of this function
is a data frame that consists of four rows containing the
following information: (1) the overall IAT effect size for the
participant's dataset, (2) the effect size calculated for odd
trials only, (3) the effect size calculated for even trials
only, and (4) the proportion of trials with reaction times
under 300ms (which is important for exclusion purposes). Items
(2) and (3) allow for a measure of the internal consistency of
the IAT. Specifically, you can use the subsetted IAT effect
sizes for odd and even trials to calculate Cronbach's alpha
across participants in the sample. The input function consists
of three arguments. First, indicate the name of the dataset to
be analyzed. This is the only required input. Second, indicate
the number of trials in your entire IAT (the default is set to
220, which is typical for most IATs). Last, indicate whether
congruent trials (e.g., flowers and pleasant) or incongruent
trials (e.g., guns and pleasant) were presented first for this
participant (the default is set to congruent). Data files
should consist of six columns organized in order as follows:
Block (0-6), trial (0-19 for training blocks, 0-39 for test
blocks), category (dependent on your IAT), the type of item
within that category (dependent on your IAT), a dummy variable
indicating whether the participant was correct or incorrect on
that trial (0=correct, 1=incorrect), and the participant’s
reaction time (in milliseconds). A sample dataset (titled
'sampledata') is included in this package to practice with.
